How big data is used as a key element for hybrid university education


  • Herbert Victor Huaranga Rivera Universidad Nacional Autónoma de Alto Amazonas, Perú
  • Milca Betsabé Herrera Aponte Universidad Nacional de Huancavelica, Perú
  • José Luis Arias Gonzales University of British Columbia, Canada
  • Milagros del Rosario Cáceres Chávez Sinfonía por el Perú, Perú
  • Katherinne Diana Magaly Itusaca Cahua Universidad Nacional de San Agustín, Perú
  • Christian Paolo Martel Carranza Universidad de Huánuco, Perú


Hybrid learning, University education, face-to-face and online learning, PRISMA


Hybrid learning in universities is the blending and mixing of the learning environments, this includes both face-to-face (FTF) which implies classroom instruction and online environment (E-learning)  as well. According to De Mauro, Greco and Grimaldi (2016), Ellis’ study shows that hybrid learning provides the students with the opportunity to understand and explore the real world at the same time through various authentic experiences. Authentic experience as cited by De Mauro, Greco and Grimaldi (2016) can be facilitated in the online learning environment through coming up with sufficient online learning or by blending learning to combine both online and FTF learning. The main objective of hybrid learning is to enhance effective and efficient experience through a more improved delivery model. This study is based on the review of previous articles using PRISMA methodology, it focuses on the big data as key element in hybrid learning in university education. The main objective of this study is to review 40 articles published in Scopus within 2010 to 2022 subject to big data in education, hybrid learning in universities or higher learning institutions and based on their findings the study come up with a conclusion  as discussed below.


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Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change, 163, 120420.

Daniel, B. K., & Butson, R. (2013). Technology Enhanced Analytics (TEA) in Higher Education. International Association for the Development of the Information Society.

De Mauro, A., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review.

Gamage, P. (2016). Big Data: are accounting educators ready? Journal of Accounting and Management Information Systems, 15(3), 588-604.

Hashem, I. A. T., Chang, V., Anuar, N. B., Adewole, K., Yaqoob, I., Gani, A., ... & Chiroma, H. (2016). The role of big data in smart city. International Journal of information management, 36(5), 748-758.

Iqbal, R., Doctor, F., More, B., Mahmud, S., & Yousuf, U. (2020). Big data analytics: Computational intelligence techniques and application areas. Technological Forecasting and Social Change, 153, 119253.

Kibria, M. G., Nguyen, K., Villardi, G. P., Zhao, O., Ishizu, K., & Kojima, F. (2018). Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE access, 6, 32328-32338.

Lv, Z., Li, X., Lv, H., & Xiu, W. (2019). BIM big data storage in WebVRGIS. IEEE Transactions on Industrial Informatics, 16(4), 2566-2573.

Miloslavskaya, N., & Tolstoy, A. (2016). Big data, fast data and data lake concepts. Procedia Computer Science, 88, 300-305.

Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018). Deep learning for IoT big data and streaming analytics: A survey. IEEE Communications Surveys & Tutorials, 20(4), 2923-2960.

Pencheva, I., Esteve, M., & Mikhaylov, S. J. (2020). Big Data and AI–A transformational shift for government: So, what next for research? Public Policy and Administration, 35(1), 24-44.

Romero, C., & Ventura, S. (2010). Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601-618.

Salminen, V., Ruohomaa, H., & Kantola, J. (2017). Digitalization and big data supporting responsible business co-evolution. In Advances in human factors, business management, training and education (pp. 1055-1067). Springer, Cham.

Sedkaoui, S., & Khelfaoui, M. (2018). Understand, develop and enhance the learning process with big data. Information Discovery and Delivery.

Viloria, A., Lis-Gutiérrez, J. P., Gaitán-Angulo, M., Godoy, A. R. M., Moreno, G. C., & Kamatkar, S. J. (2018). Methodology for the design of a student pattern recognition tool to facilitate the teaching-learning process through knowledge data discovery (big data). In International conference on data mining and big data (pp. 670-679). Springer, Cham.

Wang, H., Wang, W., Cui, L., Sun, H., Zhao, J., Wang, Y., & Xue, Y. (2018). A hybrid multi-objective firefly algorithm for big data optimization. Applied Soft Computing, 69, 806-815.

Wang, K., Mi, J., Xu, C., Zhu, Q., Shu, L., & Deng, D. J. (2016). Real-time load reduction in multimedia big data for mobile Internet. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 12(5s), 1-20.

Williamson, B. (2017). Big data in education: The digital future of learning, policy and practice. Sage.

Xu, Z., Frankwick, G. L., & Ramirez, E. (2016). Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective. Journal of Business Research, 69(5), 1562-1566.



How to Cite

Rivera, H. V. H., Aponte, M. B. H., Gonzales, J. L. A., Chávez, M. del R. C., Cahua, K. D. M. I., & Carranza, C. P. M. (2022). How big data is used as a key element for hybrid university education. International Journal of Health Sciences, 6(S1), 834–844.



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